1. Field of Invention
The present invention relates to the dissemination of healthcare outcome data.
2. Background of Invention
Healthcare expenditures exceeded $1.5 trillion in 2003, as estimated by the Centers for Medicare and Medicaid Services (CMS). This number exceeds 14% of the Gross Domestic Product (GDP), and the country should be able to expect that these enormous healthcare dollars be spent wisely. They are not. Sadly, healthcare purchasing decisions are rarely made based on service quality. This unfortunate reality occurs at every decision making level in the healthcare system. Purchasers of healthcare (e.g. employers, government), make almost all of their decisions based on cost. Payers of healthcare (e.g. insurance plans, HMOs, PPOs) empanel their networks and design prior authorization strategies to contain the cost of care. Consumers of healthcare (patients) typically have some level of choice in their “purchasing” decisions (e.g. deciding what doctor to see), but these decisions are more often based on a referral, recommendation, or convenience. Referrals are often the industry's best approximation for quality purchasing; however, referrals are more often linked to who knows whom, and an individual's prestige, rather than accurate and unbiased measures of quality. At all of these levels, the primary reason for this glaring problem in the healthcare industry is a lack of access to reliable and trusted quality data that can be easily obtained on-demand.
Reimbursable providers of healthcare (e.g. hospitals, doctors, licensed social workers, physical therapists, nurses) do have ethical and legal obligations to improve the quality of life for their patients and typically fight hard for what they believe is best for their patients. However, they are not often reimbursed for delivering good quality and their treatment decisions are more often based on how they were trained rather than what has been proven to work since they left graduate school. Federal agencies mandated to help discover and promote good healthcare (e.g. the National Institute of Health [NIH], and the Agency for Healthcare Research and Quality [AHRQ]) have current and long standing programs to help solve this “dissemination” problem—helping professionals stay current with what has proven to work best. These long-standing programs have made little inroads into the source of the problem, and it still can take 10-20 years for advances in treatment to work their way down into the mainstream. Unfortunately, it has not been a priority for healthcare providers of all types to stay current with what research has proven to work best (staying current). The few providers that do stay current are not commensurately rewarded by increased business because their success is not measured, benchmarked, or made publicly available. There is strong data and common sense to support the notion that if such data was made easily accessible to purchasers “just in time” that there would be a dramatic shift in both the quality and overall cost of healthcare. Quality would improve by natural market forces; and by eliminating ineffective and inefficient services, overall expenditures would decrease.
Most patients have some degree of choice in the selection of their healthcare providers. Some patients have complete and open access to be treated by the provider of their choice, while most have their choices limited by “quality” and cost containment strategies like network membership and authorization requirements. Nevertheless, even in the most restrictive managed care plans, patients still have choices. However, these “choices” are rarely exercised based on outcome data that can clearly identify providers that have the best chance of helping their current conditions. In most cases this data is not available to them.
The Internet is a logical platform for such data to be made accessible. In fact, the US population searches the Internet for health related content more than any other topic. Conventional “find a doctor” (FAD) services on the Internet deliver provider listings based on geographic location, typically one city at a time (e.g. the American Medical Association's FAD). To be included on such a list, providers usually must be licensed and credentialed. For most listings, these FAD services are essentially an electronic yellow page listing. For these FADs there are no current mechanisms available to sort or search based on quality.
FIG. 1 provides an overview of the prior art. In these conventional FAD services a simple database of listed providers (103) is made accessible in an output format (104) to users through simple queries (102), filtering the contents of the list by basic data like zip code. A few services add to this process some basic, unscientific patient feedback (101) like consumer satisfaction.
The few services that do deliver access to satisfaction data do so without any scientific methodology that ensures data accuracy and such services list strong disclaimers that the data is not scientifically accurate. In most cases the mode number of responders to satisfaction data is zero to two patients. The data is further biased by the self-selecting nature of the process. Only patients that make a point of returning to the website and completing a satisfaction questionnaire are included in the publicly available results. Obviously these motivated consumers are a biased sample and rarely reflect the overall quality of the provider. Furthermore, there are no safeguards that ensure that the provider and/or their staff completed these surveys and there is no ability to sort or select based on these crude outcome statistics. In essence, the feedback solicited and disseminated in these services mirror the feedback on other websites including on-line bookstores and auction houses. Rather than being an afterthought these data should be the heart of FAD services and should be designed appropriately. The present invention addresses these concerns allowing access to high quality provider ratings. In other words, FAD services must be linked to outcomes databases that are routinely providing high quality assessment and diagnostic tools to providers, as part of routine and standard care.
Another limitation in the prior art is the lack of risk-adjustment methodologies to adjust outcome data to control for the level of difficulty of each patient's problems. Some problems are easier to treat, and even with the same problem, some patients have other issues that make treating that problem more challenging. For example, treating a broken leg is more challenging if the patient is also a paraplegic and has no feeling in his extremities. Fair provider comparisons must take these variables into account and statistically risk-adjust the data accordingly.
Another significant limitation of the prior art (even when provider ratings are made available) is the often-misleading feedback that comes from overall summary scores. When a consumer is looking for a specialist to treat a specific problem, it is probably most important to receive feedback on cases with similar problems as measured by outcome tools that are tailored to the specific conditions. Overall satisfaction ratings and summary scores may provide useful supplementary information, but if a parent is trying to find the best specialist to treat her child who has life-threatening asthma and allergies, the primary concern should be what the various providers have done with similar cases in to reduce breathing problems and other related issues, not patient satisfaction or general patient quality of life. Many healthcare providers supplement their income by providing multiple services. In the asthma example above, it is quite possible that many of the specialists that could treat the child also serve as pediatricians or primary care physicians. These physicians will have patients that are being treated for routine exams, sore throats, and many other mild and transient conditions. Overall patient satisfaction ratings or general improvement of quality of life across all of these patients may be interesting and useful information to assess. However, it probably is not detailed or specific enough to help a parent or consumer make a purchasing decision. For most consumers in this situation it will be more useful to rank providers based on the measure that is most related to the specific problem (e.g. breathing). Similarly, for an HMO or PPO looking to improve their network in a specialty area, these overall ratings are fairly useless in helping to empanel the best-qualified provider for the needs of the network. Again problem specific measures (also referred to as “domain” measures by those of ordinary skill in the art) are most relevant.
In summary, electronic FAD services have great potential in helping to transform the healthcare industry that makes decisions based on quality and efficiency. However, there are significant improvements that are needed in order to fulfill this promise. Most importantly, the current art is not linked to clinically useful and accurate outcome databases, does not allow user filters or input for clinical needs and risk-adjustment variables, and does not employ the necessary algorithms to facilitate purchasing decisions based on quality.